import <- function(dir, file) {
  invisible(purrr::map(c(paste0(dir, file, ".R")), source))
}
import(
  "../prelim/ggplot2-demo/",
  c("packages", "data-clean", "covid19-lvl", "function", "plot")
)
import(
  "../prelim/echarts4r-demo/",
  c("packages", "function", "plot")
)

The Dataset

env_data <- "../data/akl-env-data.csv" %>%
  readr::read_csv(col_types = paste0("Tc", paste(rep("d", 14), collapse = ""))) %>%
  as_tsibble(index = datetime, key = location) %>%
  dplyr::mutate(
    akl_level = get_covid19level(datetime, "AKL"),
    nz_level = get_covid19level(datetime, "NZ_not_AKL"),
    covid19_period = get_covid19period(nz_level, datetime),
    lat = get_lat(location)
  )

reactable(head(env_data, 10), bordered = TRUE, striped = TRUE, highlight = TRUE)

Missing Data

dplyr::mutate(env_data, nox = cut_number(nox, 5)) %>%
  cat_heats(
    nox,
    setNames(viridis(5, option = "C"), levels(cut_number(env_data[["nox"]], 5)))
  ) +
  ggplot2::facet_grid(fct_reorder(.key, -get_lat(.key)) ~ .) +
  theme(legend.position = "top") +
  labs(col = "NOx in Auckland", x = "Date", y = "Hour of Day")

kable(
  rbind(
    unique(env_data[["location"]]),
    unique(env_data[["location"]]) %>%
      map_dbl(function(x) {
        round(mean(is.na(filter(env_data, location == x)[["nox"]])) * 100, 2)
      }) %>%
      paste0("% missing")
  )
)
customs_st glen_eden henderson khyber_pass pakuranga papatoetoe patumahoe penrose queen_street takapuna
8.15% missing 1.58% missing 9.18% missing 58.17% missing 100% missing 100% missing 5.77% missing 1.43% missing 1.9% missing 15.17% missing 
env_data <- filter(env_data, !location %in% c("pakuranga", "papatoetoe"))

Seasonal Variation

gg_botsplot(env_data, nox, outlier.shape = 1) +
  ggplot2::facet_grid(~ fct_reorder(location, -get_lat(location))) +
  labs(x = "Month", y = "NOx")

gg_seasquantile(env_data, nox) +
  ggplot2::facet_grid(~ fct_reorder(location, -get_lat(location))) +
  labs(
    x = "Month", y = "NOx",
    title = "1~100% Quantile"
  )

gg_seasquantile(env_data, nox, q = seq(.1, .9, .001)) +
  ggplot2::facet_grid(~ fct_reorder(location, -get_lat(location))) +
  labs(
    x = "Month", y = "NOx",
    title = "10~90% Quantile"
  )

Khyber Pass has the most serious traffic-induced air pollution within Auckland.

Hourly Variation within One Day

gg_seasquantile(env_data, nox, period = "day") +
  ggplot2::facet_grid(~ fct_reorder(location, -get_lat(location))) +
  theme(axis.text.x = element_text(angle = 90, vjust = .5)) +
  labs(
    x = "Time of Day", y = "NOx",
    title = "1~100% Quantile"
  )

gg_seasquantile(env_data, nox, period = "day", q = seq(.1, .9, .001)) +
  ggplot2::facet_grid(~ fct_reorder(location, -get_lat(location))) +
  theme(axis.text.x = element_text(angle = 90, vjust = .5)) +
  labs(
    x = "Time of Day", y = "NOx",
    title = "10~90% Quantile"
  )

The Effect of COVID-19 on NOx

env_data %>%
  ggplot(aes(x = akl_level, y = nox)) +
  geom_boxplot(outlier.shape = 1) +
  theme(axis.text.x = element_text(angle = 90, vjust = .5)) +
  ggplot2::facet_grid(~ fct_reorder(location, -get_lat(location))) +
  labs(
    title = "The Effect of COVID-19 Alert Levels on NOx",
    x = "COVID-19 Alert Level",
    y = "NOx"
  )

env_data %>%
  ggplot(aes(x = covid19_period, y = nox)) +
  geom_boxplot(outlier.shape = 1) +
  theme(axis.text.x = element_text(angle = 90, vjust = .5, hjust = 1)) +
  ggplot2::facet_grid(~ fct_reorder(location, -get_lat(location))) +
  ggplot2::scale_y_continuous(trans = "sqrt") +
  labs(
    title = "The Effect of Total Lockdown on NOx",
    x = "COVID-19 Period",
    y = "NOx (Square Root Scale)"
  )

filter(env_data, location == "queen_street") %>%
  update_tsibble(key = covid19_period) %>%
  gg_seasquantile(nox, q = seq(.1, .9, .001), period = "day") +
  theme(axis.text.x = element_text(angle = 90, vjust = .5)) +
  labs(
    x = "Time of Day", y = "NOx",
    title = "10~90% Quantile of NOx on Queen Street"
  )

The Effect of Location on NOx

env_data %>%
  gg_seasquantile(nox, q = seq(.1, .9, .005), polar = TRUE, size = .1) +
  facet_geo(~location, grid = akl_city_grid) +
  labs(
    title = "10~90% Quantile of NOx by Relative Geolocation",
    y = "NOx"
  )